Thanks Sean. I guess I was being pedantic. In any case if the source table does not exist as spark.read is a collection, then it is going to fall over one way or another!
On Fri, 2 Oct 2020 at 15:55, Sean Owen <sro...@gmail.com> wrote: > It would be quite trivial. None of that affects any of the Spark execution. > It doesn't seem like it helps though - you are just swallowing the cause. > Just let it fly? > > On Fri, Oct 2, 2020 at 9:34 AM Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > >> As a side question consider the following read JDBC read >> >> >> val lowerBound = 1L >> >> val upperBound = 1000000L >> >> val numPartitions = 10 >> >> val partitionColumn = "id" >> >> >> val HiveDF = Try(spark.read. >> >> format("jdbc"). >> >> option("url", jdbcUrl). >> >> option("driver", HybridServerDriverName). >> >> option("dbtable", HiveSchema+"."+HiveTable). >> >> option("user", HybridServerUserName). >> >> option("password", HybridServerPassword). >> >> option("partitionColumn", partitionColumn). >> >> option("lowerBound", lowerBound). >> >> option("upperBound", upperBound). >> >> option("numPartitions", numPartitions). >> >> load()) match { >> >> case Success(df) => df >> >> case Failure(e) => throw new Exception("Error >> Encountered reading Hive table") >> >> } >> >> Are there any performance implications of having Try, Success, Failure >> enclosure around DF? >> >>>